Autonomous Item Fabrication Utilizing a Trained Machine Learning Model

ABSTRACT

An autonomous item generation system implements a trained machine learning model configured to output fabrication instructions for generating an item and metadata describing the item, automatically and independent of user input. Fabrication instructions output by the machine learning model are transmitted to a fabrication device for generating the item. The autonomous item generation system generates a listing for the item based on the metadata output by the machine learning model and publishes the listing to a virtual marketplace. Analytics data describing feedback for the item listing is used to generate training data for the machine learning model. The training data is input to the machine learning model, which causes the machine learning model to refine at least one control parameter according to a loss function that penalizes negative differences between predicted and observed feedback data for the item. The machine learning model with the refined parameter(s) is then used by the autonomous item generation system to generate fabrication instructions and metadata for an additional item.

PRIORITY

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 17/015,822, filed on Sep. 9, 2020, which claimspriority to U.S. patent application Ser. No. 62/899,060, filed on Sep.11, 2019, the disclosures of which are hereby incorporated by referencein their entireties.

BACKGROUND

Virtual marketplaces such as network-based commerce systems areincreasingly becoming a preferred mechanism by which vendors offer goodsand services for sale, in contrast to conventional brick-and-mortarretail stores. Although the proliferation of virtual marketplacesenables vendors to reach a wider audience and offer shopping experiencesthat are curated for individual users, virtual marketplaces stillpresent significant disadvantages. For instance, while virtualmarketplaces provide item listings with certain tools to facilitatepurchase of items, such as search interfaces to browse item listings andcontrols for purchasing a subject item of an item listing, virtualmarketplaces require vendors to manually designate and design specificaspects of each item listing for publication at the virtual marketplace.

Additionally, while virtual marketplaces connect interested buyers withvendors, it remains the vendor's responsibility to manually performvarious operations required to complete the sale of an item, such asprocessing financial transactions, contracting for shipment of the itemto the buyer, and so forth. Furthermore, due to the computational andnetwork resources required to aggregate and analyze data that describespotential buyer feedback pertaining to an item listing, virtualmarketplaces only sporadically provide vendors with informationdescribing item listing feedback. Due to this limited information,vendors are unable to instantly realize how an item listing, or thesubject item, should be modified to account for changing trends andbehaviors. Consequently, vendors are faced with undue delay at eachstage in an item's lifecycle, such as item conception, item manufacture,item listing, item delivery, ascertaining item feedback, item designmodification, and so forth.

SUMMARY

To overcome these problems, a system and techniques for autonomous itemgeneration are described. An autonomous item generation system receivesat least one machine learning model trained to generate both fabricationinstructions for generating an item as well as metadata describing theitem, automatically and independent of user input. The autonomous itemgeneration system causes the at least one machine learning model togenerate the fabrication instructions and metadata for an item. Theautonomous item generation system then transmits the fabricationinstructions to a fabrication device, which causes the fabricationdevice to generate the item. A listing for the item is generated fromthe item metadata output by the at least one machine learning model, andthe autonomous item generation system publishes the listing to a virtualmarketplace. The autonomous item generation system is configured toobtain analytics data describing one or more interactions with the itemlisting as published to the virtual marketplace, such as views of theitem listing, favorites of the item listing, purchases of the item viathe item listing, navigation from the item listing to a different itemlisting, shares of the item listing, comments on the item listing, userfeedback to the item listing, combinations thereof, and so forth.

In some implementations, the autonomous item generation system isconfigured to serve as the virtual marketplace by publishing the itemlisting to a network (e.g., the Internet) and monitoring network trafficpertaining to the item listing. Alternatively or additionally, theautonomous item generation system is configured to leverage an existingvirtual marketplace platform and implement one or more applicationprogramming interfaces (APIs) of the virtual marketplace to causepublication of the item listing on the virtual marketplace and obtainanalytics data pertaining to the item listing from the virtualmarketplace. Based on the analytics data, and optionally additionalfeedback data pertaining to the item, the autonomous item generationsystem forms training data for refining the at least one machinelearning model. The training data is provided as input to the at leastone machine learning model, which causes modification of one or morecontrol parameters of the at least one machine learning model. Theautonomous item generation system then generates fabricationinstructions and metadata for an additional item using the at least onemachine learning model with its modified parameter(s) and repeats itsoperations to continuously refine the machine learning model(s), withoutrequiring user input or intervention to do so.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures.

FIG. 1 is an illustration of an environment in an example implementationthat is operable to employ the autonomous item generation techniquesdescribed herein.

FIG. 2 depicts an example implementation in which an autonomous itemgeneration system of FIG. 1 generates an item and an item listing forthe item using at least one machine learning model and refines the atleast one machine learning model based on feedback data for the item.

FIG. 3 depicts an example implementation of a user interface displayingan item listing generated by the autonomous item generation system ofFIG. 1 .

FIG. 4 depicts an example implementation of a user interface displayingan item listing generated by the autonomous item generation system ofFIG. 1 .

FIG. 5 depicts an example implementation of a user interface for theautonomous item generation system of FIG. 1 .

FIG. 6 depicts an example implementation of a user interface for theautonomous item generation system of FIG. 1 .

FIG. 7 is a flow diagram depicting a procedure in an exampleimplementation for the autonomous item generation system of FIG. 1generating an item using at least one machine learning model andrefining the machine learning model for generating additional items.

FIG. 8 is a flow diagram depicting a procedure in an exampleimplementation for outputting and modifying a user interface for theautonomous item generation system of FIG. 1 .

FIG. 9 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilized with reference to FIGS. 1-8 to implementembodiments of the techniques described herein.

DETAILED DESCRIPTION Overview

With advances in computing device technology, virtual marketplaces areincreasingly used as a mechanism to publish item listings (e.g., offersof goods for sale). While such virtual marketplaces enable vendors toreach a wider audience than otherwise enabled by conventionalbrick-and-mortar storefronts, vendors continue to face undue burdensinvolved with bringing an initial concept design to a tangible item,determining a strategy for listing the item on a virtual marketplace,publishing the listing to the virtual marketplace, entering intotransactions with potential buyers, securing shipping for transportingthe item to a buyer, and verifying that the item actually reaches thebuyer. For instance, from designers tasked with creating item designs,manufacturers tasked with ensuring the resulting item matches the item'sdesign, marketers tasked with creating listings for the item, and salesrepresentatives tasked with ensuring the item reaches the buyer, each ofthese various actions required to develop an item from conception to atangible good requires substantial human input and is thus prone tohuman error and delay.

These inefficiencies are further compounded when considering thelifecycle of an item's development, which requires further time andresources to ascertain feedback describing the public's reaction to theitem before determining modifications to be incorporated in subsequentitem design iterations. For instance, due to the computational andnetwork resources required to collect, store, and analyze virtualmarketplace user behavior information, virtual marketplaces aredissuaded from continuously analyzing and publishing user behaviorinformation due to the prohibitive amount of resources required to doso. This delay in aggregating and providing user behavior informationforces vendors to wait for publication of the information, and furtherspend significant time analyzing the behavior information in the hope ofidentifying certain characteristics of the item design, the itemlisting, or other aspects in the pipeline of item conception to deliverythat can benefit from improvement. Furthermore, an accuracy of suchvendor analysis is dependent on an expertise level of individuals taskedwith gleaning trends from user behavior information, and even the mostskilled team of individuals is unable to simultaneously handle analysisfor multiple different items, much less an entire item catalog.

Accordingly, systems and techniques are described herein that enableautonomous item generation, in which generation of instructions forfabricating, manufacturing, or otherwise creating a tangible item andgeneration of a listing for the tangible item at a virtual marketplaceare performed automatically, and independent of user input. To do so, anautonomous item generation system employs at least one machine learningmodel trained to generate, for a tangible item, item data that includesfabrication instructions for the item, a description for the item, tagsdescribing various attributes of the item, and a recommended price forlisting the item at a virtual marketplace. In addition to automaticallygenerating the fabrication instructions and listing for the item, theautonomous item generation system is further configured to interfacewith the virtual marketplace to automatically perform operationsinvolved with transporting the item from a fabrication device used togenerate the item to a purchasing entity, including financialtransactions, shipping operations, and so forth.

The autonomous item generation system is further configured to obtainfeedback data describing one or more interactions with the item listingas published to the virtual marketplace, and continuously modify controlparameters of the machine learning model used to generate the item anditem listing, all without human user intervention or guidance. In thismanner, the autonomous item generation system is configured to identifyvirtual marketplace trends and behaviors in real-time, well before eventhe most skilled human analyst could detect the same trends andbehaviors when provided with the same behavior data. Thus, theautonomous item generation system and techniques described herein areconfigured to generate items and item listings without human guidance,identify observed interactions with the product listings, andcontinuously adapt to such observed interactions in generatingsubsequent items and item listings at both a rate and a scale that isnot possible via conventional systems.

Example Environment

FIG. 1 illustrates a digital medium environment 100 in an exampleimplementation that is operable to employ the autonomous item generationtechniques described herein. The illustrated environment 100 includescomputing device 102, which may be implemented according to a variety ofdifferent configurations. The computing device 102, for instance, may beconfigured as a desktop computer, a laptop computer, a mobile device(e.g., assuming a handheld configuration such as a tablet or mobilephone), and so forth. Thus, the computing device 102 may range from fullresource devices with substantial memory and/or processing resources todevices with limited memory and/or processing resources (e.g., mobiledevices). Additionally, although a single computing device 102 is shown,the computing device 102 may be representative of a plurality ofdifferent devices, such as multiple servers utilized by a business toperform operations “over the cloud” as described in further detail belowwith respect to FIG. 9 .

The computing device 102 is illustrated as including an autonomous itemgeneration system 104. The autonomous item generation system 104 isimplemented at least partially in hardware of the computing device 102and represents functionality of the computing device 102 to generate anitem 106 and an item listing 108 publication at a virtual marketplace,automatically and independent of user input, guidance, instruction, orother form of intervention that facilitates generation of the item 106or item listing 108. In this manner, the item 106 is representative of atangible good, product, and the like. In some implementations, the item106 is representative of digital content, such as a digital graphic, ananimation, a video, and so forth. The item listing 108 is representativeof publication information describing the item 106, as described infurther detail below.

To enable generation of the item 106 and the item listing 108, theautonomous item generation system 104 employs an item generation module110, a transaction module 112, a feedback module 114, and a trainingmodule 116. The item generation module 110, the transaction module 112,the feedback module 114, and the training module 116 are implemented atleast partially in hardware of the computing device 102 (e.g., throughuse of a processing system and computer-readable storage media), asdescribed in further detail below with respect to FIG. 9 .

The item generation module 110 is representative of functionality of thecomputing device 102 to generate the item 106 and item listing 108,without user input otherwise required by conventional systems (e.g.,input specifying design criteria for the item 106, input specifyinginformation to be included or emphasized in the item listing 108, inputspecifying demographic targeting information for the item listing 108,and so forth). To do so, the item generation module 110 employs machinelearning model 118. Machine learning model 118 is representative of oneor more machine learning models, where each model represented by machinelearning model 118 can be configured according to a range of differentmachine learning model architectures. For instance, machine learningmodel 118 is representative of a model having an architecture based onneural networks (e.g., fully-connected neural networks, convolutionalneural networks, or recurrent neural networks), deep learning networks,generative adversarial networks (GANs), decision trees, support vectormachines, linear regression, logistic regression, Bayesian networks,random forest learning, dimensionality reduction algorithms, boostingalgorithms, combinations thereof, and so forth.

Regardless of an underlying machine learning model architecture, machinelearning model 118 is representative of one or more trained machinelearning models that are configured to generate fabrication instructions120 for the item 106 and metadata describing the item 106, where themetadata is useable by the autonomous item generation system 104 togenerate the item listing 108. For instance, the machine learning model118 may be representative of a GAN that is trained to generatefabrication instructions for a particular type of item (e.g., an articleof clothing, a work of art, a three-dimensional model, and so forth).Such a trained GAN implementation of the machine learning model 118 maybe generated by providing the machine learning model 118 with aplurality of training sets, where each training set includes informationthat is useable to guide the machine learning model towards producing adesired output.

For instance, in the context of a GAN trained to generate fabricationinstructions and metadata for an article of clothing, each trainingdataset may include fabrication instructions for an example article ofclothing (e.g., instructions describing one or more fabrics or materialsto be used in generating the article of clothing; cut patterns for theone or more fabrics or materials; adhesion instructions for combiningthe cuts of materials or fabrics such as sewing patterns, sewing threadtypes, fabric adhesive types, and the like; folding instructions for thearticle of clothing; and so forth). In addition to fabricationinstructions for the example article of clothing, the training datasetmay further include metadata describing the example article of clothing(e.g., a name for the article of clothing, a product category for thearticle of clothing, descriptive attributes of the article of clothing,a demographic audience for the article of clothing, and so forth). Eachtraining dataset for the example machine learning model 118 trained togenerate fabrication instructions and metadata for an article ofclothing may further include information describing feedback pertainingto the subject article of clothing for the training dataset. Forinstance, such feedback information may specify a number of times alisting for the article of clothing was viewed via a virtualmarketplace, a number of purchases made of the article of clothing, apercentage of views that resulted in a share or favorite of the articleof clothing, reviews for the article of clothing, information specifyingcomparative feedback data for different articles of clothing listed onthe virtual marketplace, information describing an appearance of alisting for the article of clothing on the virtual marketplace,combinations thereof, and so forth.

In this manner, the machine learning model 118 is representative of amodel trained on a generic dataset for one or more characteristics to belearned by the model during training. For instance, continuing theexample of a machine learning model 118 trained to learn artworkcharacteristics, the machine learning model 118 is representative of amodel trained on a training dataset that includes a collection ofdifferent works of art that share one or more common characteristics(e.g., being portraits of a human subject, depicting landscapes, beingabstract art, being vector artwork, comprising a certain color palette,and so forth). As such, the characteristics learned by machine learningmodel 118 during training are dictated by the contents of the datasetused during training, such that the training dataset defines a style ortheme of the machine learning model's 118 output following training.

Accordingly, the machine learning model 118 may be representative of amodel trained on a training dataset that consists of human portraits,such that the machine learning model 118 is configured to output artworkthat depicts human portraits after training is complete. In someimplementations, machine learning model 118 is representative of one ormore models trained on a plurality of different training datasets. Forinstance, continuing the previous example of a training datasetconsisting of human portraits, after training the machine learning model118 to output artwork depicting human portraits, training may continueusing an additional training dataset that includes artwork depictinglandscapes, such that the machine learning model 118 is further trainedto output artwork depicting human portraits against landscapebackgrounds.

In a similar manner, the machine learning model 118 may berepresentative of a plurality of different machine learning modelsarranged in a stacked configuration, where the output of one model isprovided as input to a different model of the stacked configuration. Insuch an example implementation, each model of the stacked configurationof machine learning models may be trained on a different trainingdataset, such as one trained to output landscape artwork, anothertrained to output abstract artwork, and another trained to outputwatercolor artwork. Continuing this example stacked configuration,landscape artwork output by the initial model would be provided as inputto the model trained to output abstract artwork, which would output anabstract artwork representation of the input landscape, which in turnwould be provided to the watercolor artwork-trained model, such that theresulting output of the stacked configuration of machine learning modelsis an abstract watercolor landscape work of art. Thus, the specificcharacteristics learned by the machine learning model 118 are dependenton the training dataset(s) used to generate the machine learning model118 and are not restricted to the examples provided herein.

As an additional example, in an implementation where the machinelearning model 118 represents a GAN trained to generate fabricationinstructions and metadata for a piece of art, each training dataset mayinclude fabrication instructions for a specific piece of art (e.g.,instructions designating one or more materials to use in generating thepiece of art such as paint type and color, ink, paper, canvas,combinations thereof, and the like; printing instructions for generatingthe piece of art on a particular medium; dimensional constraints for thepiece of art; and so forth). Individual training datasets supplement thefabrication instructions for the piece of art by including metadatadescribing the particular piece of art (e.g., a title for the piece ofart, a description of the art, tags for listing the piece of art in avirtual marketplace, a recommended price for the piece of art,combinations thereof, and so forth). Each training dataset may furtherinclude feedback information for the piece of art, such as feedbackinformation similar to that described above with respect to the exampletraining dataset for training the machine learning model 118 to generatefabrication instructions for an article of clothing.

Given such example training datasets, in an example implementation wherethe machine learning model 118 is configured as a GAN, the GAN may betrained by causing different portions of the GAN (e.g., a generatorportion and a discriminator portion) to compete in an adversarialobjective (e.g., a min-max game) that seeks to maximize positivefeedback associated with a corresponding item 106 generated according tofabrication instructions 120 output by the GAN. For instance, thefeedback data of the example training datasets may be normalized on ascale that indicates whether feedback data for an item is generallypositive or negative (e.g., feedback data indicating numerous views,purchases, shares, positive reviews of the item may be characterized andquantified as indicating positive feedback for the subject item of thetraining dataset).

Such positive feedback data can be contrasted with feedback dataindicating few purchases, shares, or favorites of the item, feedbackdata indicating negative user reviews, and/or feedback data indicating aview of the item and subsequent purchase of a different similar item,which may be characterized and quantified as indicating negativefeedback for the subject item of the training dataset. Under a trainingobjective function, the machine learning model 118 may be configured togenerate fabrication instructions 120 and metadata for an item 106 in amanner that seeks to maximize positive feedback data for the item 106.

In some implementations, training the machine learning model 118includes supplementing training data from the training datasets withnoise (e.g., Gaussian input noise), which causes the generator portionof the GAN to generate samples that could potentially fool thediscriminator portion in the mini-max game objective example. In thismanner, the machine learning model 118 is representative of one or moremachine learning models that are trained to identify different aspectsof item fabrication instructions and/or descriptive metadata for theitem that influences positive feedback associated with the item.

During training on an initial dataset, the machine learning model 118 isinstructed (e.g., via an objective function using convolutional neuralnetworks) to generate an output defined by characteristics of a trainingdataset (e.g., articles of clothing with long sleeves). Outputs of themachine learning model 118 are then compared to the training dataset,and the model is governed to generated realistic articles of clothingwith long sleeves based on a loss function determined from thecomparison (e.g., F1 loss, visual perceptual quality loss, combinationsthereof, and so forth in an implementation where the machine learningmodel 118 is configured as a GAN). Training continues until the machinelearning model 118 converges and consistently generates outputs that arewithin a comparative threshold to the training dataset, at which pointthe machine learning model 118 is output, or adapted to differentcharacteristics using additional training datasets.

In some implementations, the machine learning model 118 is furthertrained to identify differences in feedback data associated with an itemamong different demographic segments. For instance, training data mayindicate that for a same article of clothing (e.g., a down jacket), thearticle of clothing is generally associated with positive feedback for aparticular geographic location demographic segment during a three monthwindow (e.g., during a winter season for the particular geographiclocation) and is generally associated with negative feedback for thenorthern hemisphere demographic segment at other times (e.g., duringspring, summer, and fall seasons for the particular geographiclocation). In this manner, the machine learning model 118 is furthertrained with the objective of maximizing positive feedback associatedfor an item at an audience-specific level, where the audience can beconstrained according to any range of control parameters (e.g.,geographic location, time of day, day(s) of week, audience age, audiencegender, combinations thereof, and so forth).

In some implementations, the machine learning model 118 may be receivedby the autonomous item generation system 104 together with an indicationof control parameters in the machine learning model's 118 latentspace(s). Alternatively or additionally, the training module 116 isconfigured to identify one or more control parameters in the machinelearning model's 118 latent space(s). Such control parameters correlateto any aspect of the machine learning model's 118 output. For instance,in an example where the machine learning model 118 is configured tooutput works of art depicting landscapes, one control parameter mayaffect a size of the sky in the resulting landscapes, another controlparameter may define a color palette (e.g., one or more colors) used indepicting mountains in the landscape, another control parameter maydescribe a medium on which the landscape is depicted, another controlparameter may describe characteristics of a demographic audience forwhich the landscape is generated, and so forth. In implementations wherethe machine learning model 118 is received by the autonomous itemgeneration system 104 without an indication as to which controlparameters correlate with specific output characteristics, the trainingmodule 116 is configured to identify control parameters by adjustingindividual control parameters of the machine learning model 118 anddetermining how the adjustment affects the resulting model output.

Thus, regardless of architectural configuration of the machine learningmodel 118, the machine learning model 118 is representative of one ormore trained machine learning models that are configured to generatefabrication instructions 120 for the item 106 and metadata describingthe item 106 that is useable by the autonomous item generation system104 to generate the item listing 108, in a manner that seeks to maximizepositive feedback associated with the item 106.

Upon generating fabrication instructions 120 for the item 106 using themachine learning model 118, the item generation module 110 is configuredto transmit the fabrication instructions 120 to a fabrication device122, which is representative of one or more machines that are configuredto generate the item 106, responsive to receipt of the fabricationinstructions. For instance, in an example implementation where the item106 is an article of clothing, the fabrication device 122 isrepresentative of one or more textile machines, such as a textilesourcing machine, a textile spinning machine, a textile finishingmachine, cloth finishing machine, a knitting machine, a fabric seamingmachine, a crochet machine, a quilting machine, a tufting machine, aweaving machine, a component (e.g., zipper, button, etc.) manufacturingmachine, a measuring machine, a cutting machine, an embroidery machine,a sewing machine, a washing machine, a drying machine, a foldingmachine, a monogramming machine, an applique attachment machine,combinations thereof, and the like.

As another example, in an implementation where the item 106 is a pieceof art, the fabrication device 122 may be configured as one or more of atwo-dimensional printer, a three-dimensional printer, a computernumerical control (CNC) machine, combinations thereof, and so forth. Asyet another example, in an implementation where the item 106 isrepresentative of digital content, the fabrication device may berepresentative of computer-aided design (CAD) software implemented atleast partially in hardware of a computing device, such as in hardwareof the computing device 102. Thus, the fabrication device 122 isrepresentative of any one or combination of multiple devices that arecapable of generating the item 106 based on the fabrication instructions120 output by the machine learning model 118.

The transaction module 112 is representative of functionality of thecomputing device 102 to generate the item listing 108 for the item 106,based on the metadata describing the item 106 as output by the machinelearning model 118. The item listing 108 generated by the transactionmodule 112 is representative of information that describes an appearanceof the item listing 108 as published at a virtual marketplace 124, bothas visually appearing to a viewing user of the marketplace 124 as wellas appearing in data observed by a search engine (e.g., when indexingvirtual marketplace 124 or otherwise becoming aware of the item listing108). For instance, the item listing 108 is representative of dataspecifying at least one of a title for the item 106, a detaileddescription for the item 106, a representative image for the item 106, asuggested price for the item 106, one or more different items that aresimilar to the item 106, combinations thereof, and so forth. Exampleimplementations of an item listing 108 generated by the transactionmodule 112 are illustrated in FIGS. 3 and 4 and described in furtherdetail below.

The transaction module 112 is further representative of functionality ofthe computing device to interface with the virtual marketplace 124 ordirectly implement the virtual marketplace as part of the autonomousitem generation system 104. For instance, the virtual marketplace 124 isrepresentative of a service configured to publish item listings 108where items (e.g., tangible goods) are offered for sale. In someimplementations, the virtual marketplace 124 is representative of asocial networking system or other type of informational system that isconfigured to output the item listing 108 for display to one or moreusers. The virtual marketplace 124 may be hosted on dedicated or sharedserver machines (not shown) that are communicatively coupled to enablecommunications between the server machines. In implementations where thevirtual marketplace 124 is implemented at the computing device 102, thevirtual marketplace 124 may be implemented over distributed servers asdescribed in further detail below with respect to FIG. 9 . In thismanner, the virtual marketplace 124 is further representative ofconnected components that allow the components to share and accesscommon data, such as data hosted on one or more databases.

The virtual marketplace 124 is further representative of a platform thatprovides at least one of a publishing mechanism, a listing mechanism, ora price-setting mechanism that enable a seller to list, or publishinformation pertaining to tangible goods for sale. In a similar manner,the virtual marketplace 124 is representative of a platform that enablesa buyer to express interest in, or indicate a desire to purchase, thetangible goods offered for sale. In this manner, the virtual marketplace124 may comprise at least one publication engine and at least oneshopping engine. The publication engine of the virtual marketplace isassociated with one or more Application Programming Interfaces (APIs)that enable the transaction module 112 to communicate the item listing108 to the virtual marketplace 124 and cause the virtual marketplace 124to publish the item listing 108 in a manner that can be observed andinteracted with by a user of the virtual marketplace (e.g., a potentialbuyer of the item 106). The shopping engine of the virtual marketplace124 is associated with one or more APIs that enable a user of thevirtual marketplace 124 to accept an offer for sale of the item 106 byagreeing to pay a price associated with the item 106. In someimplementations, the APIs supported by the shopping engine of thevirtual marketplace 124 support different price listing formats forpublication of the item listing 108. As an example, price listingformats include fixed-price listing formats (e.g., the traditionalclassified advertisement-type listing or a catalog listing),auction-type price listing formats, buyout-type listing formats (e.g.,the Buy-It-Now (BIN) technology developed by eBay Inc., of San Jose,Calif.), combinations thereof, and so forth.

The virtual marketplace 124 is further representative of a navigationengine that enables a user of the virtual marketplace to browse andinspect various item listings 108 published by the virtual marketplace124. For instance, the navigation engine of the virtual marketplace 124enables a user to identify and discover various item listings byproviding a search module that enables keyword and/or image searches ofitem listings 108 or other information published by the virtualmarketplace 124. In some implementations, the virtual marketplace 124may organize item listings 108 according to various data structures(e.g., category, catalog, or other form of classification fordifferentiating and grouping item listings 108, relative to oneanother). In this manner, the virtual marketplace 124 may provide toolsthat enable users to browse published item listings 108 according tometadata tags that categorize the item listing 108, rather than havingto index through an entirety of item listings 108 published by thevirtual marketplace. Various other navigation techniques and itemlisting classification and categorization approaches may be enabled bythe virtual marketplace 124 without departing from the spirit and scopeof the examples described herein.

The virtual marketplace 124 is further representative of a messagingsystem that enables generation and delivery of various entities involvedwith facilitating a transaction via the virtual marketplace 124. Forinstance, the messaging system implemented by the virtual marketplace124 may facilitate communications among a selling entity that publishedthe item listing 108 to the virtual marketplace, a purchasing entity 126that purchases the item 106 via interaction with the item listing 108, ashipping entity (not shown) contracted to physically deliver the item106 from the fabrication device 122 to the purchasing entity 126, one ormore financial institutions tasked with transferring funds among thevarious entities (e.g., the autonomous item generation system 104, thevirtual marketplace 124, the fabrication device 122, the shippingentity, the purchasing entity 126, and so forth).

Communication of data among these various entities involved infacilitating the fabrication of the item 106, the publication of theitem listing 108 to the virtual marketplace, the contracting for sale ofthe item 106 to the purchasing entity 126, the delivery of the item 106to the purchasing entity 126, and the transfer(s) of funds among thevarious entities is enabled via the network 128. The network 128 isrepresentative of a real time communication protocol that connects theautonomous item generation system 104 to one or more of the fabricationdevice 122, the virtual marketplace 124, the purchasing entity 126, oneor more shipping entities, and one or more financial institutionsinvolved in these example activities. For instance, the network 128 mayrepresent functionality of a real-time communication protocol, such as aremote procedure call that enables a streaming, always-connected linkamong different entities. Alternatively or additionally, the network 128may be representative of the Internet, a subscriber network such as acellular of Wi-Fi network, combinations thereof, and so forth.

The feedback module 114 is representative of functionality of thecomputing device 102 to obtain analytics data from the virtualmarketplace 124, such as information describing user feedback pertainingto the item listing 108 as published at the virtual marketplace 124.Data obtained by the feedback module 114 is representative of any formof information that indicates a manner in which the item listing 108 wasobserved and/or interacted with by users of the virtual marketplace 124.For instance, the feedback module 114 may include one or more APIsconfigured to obtain data describing a number of views (e.g., a numberof impressions) of the item listing 108, a number of purchases of theitem via the item listing 108, a number of favorites of the item listing108, a number of shares of the item listing 108, user reviews submittedfor the item listing 108, combinations thereof, and so forth.

The feedback module 114 is further representative of functionality ofthe autonomous item generation system 104 to obtain user profileinformation (e.g., age, gender, location, and so forth) pertaining toindividual users that interacted with the item listing 108, and metadatadescribing the interaction (e.g., a duration of the interaction,specific aspects of the item listing 108 with which a user interacted,an amount of time spent viewing certain portions of the item listing, adate and time of the interaction, combinations thereof, and so forth).Thus, the feedback module 114 is representative of functionality of theautonomous item generation system 104 to understand how the particularitem listing 108 for the item 106 was received, relative to other itemlistings published on the virtual marketplace 124.

The training module 116 is representative of functionality of theautonomous item generation system 104 to modify the machine learningmodel 118 by modifying at least one control parameter of the machinelearning model 118 based on data obtained by the feedback module 114pertaining to the item listing 108. In this manner, the training module116 is configured to generate additional training datasets for refiningthe machine learning module 118, where each additional training datasetincludes data describing the item listing 108 and the fabricationinstructions 120 for the item 106 along with at least one instance ofanalytics data obtained by the feedback module 114 describing a userinteraction with the item listing 108 at the virtual marketplace 124.

For instance, the training module 116 may generate a training datasetthat includes the control parameters for the machine learning model 118used in generating the fabrication instructions 120 and metadata for theitem 106 (e.g., metadata used to generate the item listing 108). Thetraining module 116 supplements the control parameters in the trainingdataset with predicted feedback data for the item 106, and provides thetraining dataset to the machine learning model 118 as input along with aloss function that penalizes differences, between predicted feedbackdata of the training dataset and observed feedback data obtained fromthe feedback module 114, indicating that the control parameters resultedin negative feedback for the item 106. By penalizing differencesindicating negative feedback relative to predicted and observed feedbackdata (e.g., differences indicating that a predicted average user reviewscore for the item 106 is higher than an observed average user reviewscore for the item 106), the loss function implemented by the trainingmodule 116 rewards differences indicating that feedback pertaining tothe item 106 was more positive than predicted.

Through training based on such example training datasets compared toobserved feedback data, the training module 116 is configured to causethe machine learning model 118 to output ideal control parameters foruse in generating fabrication instructions 120 and metadata useable tocreate an item listing 108 for a subsequent iteration of the item 106.In some implementations, control parameters output through this trainingprocess may be configured as a ranking of different combinations ofcontrol parameters for the machine learning model 118, where the rankingis ordered based control parameter combinations that are likely togarner the most positive feedback via publication to the virtualmarketplace 124.

The training module 116 is configured to refine control parameters ofthe machine learning model 118 using any number of different lossfunctions. For instance, loss functions may be layered, such that theloss function penalizing negative differences between predicted andobserved user review average scores is layered with a loss function thatpenalizes negative feedback differences between predicted and observeduser interactions with the item listing 108 (e.g., click-through rates,purchase rates, etc.). In some implementations, in addition to trainingthe machine learning model 118 using loss functions that considerdifferences between predicted and observed feedback data, the trainingmodule 116 is configured to employ one or more multi-armed banditapproaches to explore novel control parameter combinations that differfrom previously attempted control parameter combinations for the machinelearning model 118.

In this manner, the training module 116 is configured to continuouslyrefine the machine learning model 118 to adapt its subsequent generationof fabrication instructions 120 and item metadata for use in generatingthe item listing 108 for additional items 106, while accounting for userbehaviors and trends at the virtual marketplace 124. By enablingperformance of the item generation module 110, the transaction module112, the feedback module 114, and the training module 116 to beaccomplished independent of user input or other intervention that guidesthe generation of the fabrication instructions 120 or design of the itemlisting 108, the autonomous item generation system 104 advantageouslyenables real-time adaptation to changes in user behavior and trends atthe virtual marketplace 124 at a rate that is impossible to achieve byconventional systems that require human input or intervention. Theadvantages enabled by the autonomous item generation system 104 relativeto conventional approaches are exponentially increased when generating acatalog of items 106 and corresponding item listings 108, as the humanhours required by conventional approaches prohibit generatingfabrication instructions 120 and item listings 108 for an item 106 inreal-time.

Having considered an example digital medium environment, consider now adiscussion of example implementations of autonomously generating an itemand an item listing for the item using the techniques described herein.

FIG. 2 depicts a system 200 in an example implementation showingoperation of the autonomous item generation system 104 of FIG. 1 ingreater detail as generating an item 106 and an item listing 108,automatically and independent of user input via the machine learningmodel 118, and as refining the machine learning model 118 based on datadescribing one or more interactions with the item listing 108 aspublished to the virtual marketplace 124. To do so, system 200illustrates components of the autonomous item generation system 104,including the item generation module 110, the transaction module 112,the feedback module 114, and the training module 116. The itemgeneration module 110 is configured to cause the machine learning model118 to output item data 202 for the item 106, where a type of the item106 depends on an objective and training dataset used to originallytrain the machine learning model 118.

The item data 202 includes the fabrication instructions 120 for the item106 along with metadata for the item 106 including an item description204, at least one item tag 206, and item pricing data 208. The itemdescription 204 is representative of a title for the item 106 to beincluded in the item listing 108, a detailed textual description for theitem listing 108, and a digital rendering (e.g., an image, a video, ananimation, combinations thereof, and so forth) of the item 106 to berepresented in the item listing 108. The item tags 206 included in theitem data 202 are representative of metadata to be embedded in the itemlisting 108 that enables the virtual marketplace 124 and/or a searchengine (not shown) to identify and categorize the item listing 108(e.g., relative to other item listings published at the virtualmarketplace 124).

Item pricing data specifies at least one suggested price to beassociated with the item 106 (e.g., to be displayed as part of the itemlisting 108). In some implementations, the item tags 206 further specifyaudience information for the item listing 108 to define a particularmanner in which the item listing 108 is published at the virtualmarketplace 124. For instance, the item tags 206 may include informationspecifying a particular demographic for the item listing 108 thatrestricts its publication to the particular demographic (e.g.,specifying different item pricing data 208 for European and Asianmarkets, specifying different visual appearances for conveying the itemdescription 204 in the item listing 108 at different times of the day,and so forth). Thus, the item data 202 generated by the machine learningmodel 118 is representative of information that is useable by thefabrication device 122 to fabricate the item 106 as well as informationthat is useable by the transaction module 112 to generate the itemlisting 108.

Upon receiving the item data 202 from the item generation module 110,the transaction module 112 is configured to generate the item listing108, where a visual appearance of the item listing 108 as published tothe virtual marketplace 124 is defined by one or more of the itemdescription 204, the item tags 206, or the item pricing data 208. For anexample representation of an item listing 108 generated by theautonomous item generation system 104, consider FIG. 3 .

FIG. 3 depicts an example interface 300 of the virtual marketplace 124as displaying an item listing 302. In the illustrated example of FIG. 3, the item listing 302 represents an instance of the item listing 108generated by the transaction module 112, where the item listing 302 iscreated for an article of clothing item 106 generated by the machinelearning model 118. Specifically, the item listing 302 includes an itemtitle 304 for a “Men's Casual Button Down Shirt,” and a digitalrendering 306 of the item 106. The digital rendering 306 indicates howthe item 106 would visually appear following fabrication by thefabrication device 122, according to the fabrication instructions 120.The item listing 302 further includes a price 308 for purchasing theitem 106 depicted by the digital rendering 306 and a detaileddescription 310 that provides a viewing user with additional informationdescribing the item 106.

The example item listing 302 further includes an item details portion312 configured to display additional information not provided by theitem title 304, the digital rendering 306 of the item, the price 308, orthe detailed description 310. For instance, in the example context ofthe item 106 being an article of clothing, the item details 312 mayspecify specific dimensions of the article of clothing, textiles used toconstruct the article of clothing, and any other information included inthe item data 202 output by the machine learning model 118.

The item listing 302 is further illustrated as including a shippingoptions portion 314, a user reviews portion 316, and a similar itemsportion 318. The shipping options portion 314 is representative ofinformation displayed to a viewing user of the item listing 302 thatinforms the viewing user as to available choices for logisticallytransferring the subject item 106 of the item listing 302 from thefabrication device 122 that manufactures the item 106 to a location ofthe viewing user (e.g., the purchasing entity 126). The user reviewsportion 316 is representative of explicit feedback informationpertaining to the item 106 as received from one or more users of thevirtual marketplace 124 that have previously purchased the item 106. Thesimilar items portion 318 is configured to display representations 320,322, and 324 of different item listings published to the virtualmarketplace 124 that are identified as being similar to the item 106 forwhich the item listing 302 is generated (e.g., based on comparison ofthe item tags 206 for the item 106 to tags associated with therepresentations 320, 322, and 324).

In some implementations, the shipping options portion 314, the userreviews portion 316, and the similar items portion 318 of the itemlisting 302 are defined by the virtual marketplace 124 to which the itemlisting is published, rather than being defined by the transactionmodule 112. Alternatively, the transaction module 112 may be configuredto control a visual appearance of one or more of the shipping optionsportion 314, the user reviews portion 316, or the similar items portion318 by virtue of a communicative connection between the virtualmarketplace 124 and the transaction module 112 (e.g., network 128), asrepresented by the double-headed arrow connecting the transaction module112 and the virtual marketplace 124 in FIG. 2 .

The item listing 302 further includes controls 326, 328, and 330 forinteracting with the item listing 302 via the virtual marketplace 124,where interaction with the controls 326, 328, and 330 is indicative ofpositive feedback to the item listing 302. For instance, control 326enables a viewing user to immediately purchase the subject item 106 ofthe item listing 302, control 328 enables the viewing user to add theitem 106 to a shopping cart during browsing of the virtual marketplace,and control 330 enables the viewing user to favorite the item 106. Inthis manner, controls 326, 328, and 330 are representative aspects ofthe item listing 302 from which interaction data may be gleaned toascertain a positive or negative reaction to the item listing and usedas feedback data for further refining control parameters of the machinelearning model 118 used to generate the item listing 302 and its subjectitem 106.

Returning to FIG. 2 , the transaction module 112 is illustrated asincluding a listing component 210, a finance component 212, and alogistics component 214. In implementations where the virtualmarketplace 124 is implemented as part of the autonomous item generationsystem 104, the listing component 210, the finance component 212, andthe logistics component 214 are representative of the transactionmodule's 112 ability to enable functionality of the standalone virtualmarketplace 124, as described above with reference to FIG. 1 .Alternatively, in implementations where the virtual marketplace 124 isimplemented independently from the autonomous item generation system104, the listing component 210, the finance component 212, and thelogistics component 214 are representative of functionality of theautonomous item generation system to automatically handle interactionswith the virtual marketplace 124 that otherwise cannot be performed byconventional systems absent human user intervention.

For instance, the listing component 210 is representative offunctionality of the transaction module 112 to communicate and causepublication of the item listing 108 at the virtual marketplace 124. Inaccordance with one or more implementations, the listing component 210is representative of one or more APIs configured to interface with thevirtual marketplace 124 and list the item 106 according to one or moreshopping engines or price-listing platforms supported by the virtualmarketplace 124. The finance component 212 is representative offunctionality of the transaction module 112 to interface with one ormore financial institutions to transfer funds among the various entitiesinvolved in facilitating the fabrication of the item 106, publishing theitem listing 108, purchasing the item 106, and facilitating shipment ofthe item 106 to a purchasing entity 126. In some implementations, thefinance component 212 is configured to handle returns and processrefunds in the event a purchasing entity 126 is dissatisfied with theitem 106 and attempts to return the item 106 via the virtual marketplace124.

In a similar manner, the logistics component 214 is representative offunctionality of the transaction module 112 to identify one or moreshipping options for logistically transporting the item 106 to apurchasing entity 126. For instance, the logistics component 126 isconfigured to identify geographic locations associated with afabrication device 122 that manufactured the item 106 and the purchasingentity 126 to which the item 106 is to be transported. Given thegeographic locations, the logistics component 214 is configured tointerface with one or more shipping entities to obtain quotes for costsassociated with transporting the item 106 to the purchasing entity 126.In some implementations, the logistics component 214 is configured toupdate the item listing 108 to convey such shipping cost quotes for aparticular purchasing entity 126 viewing the item listing (e.g., byupdating information included in the shipping options portion 314 of theexample item listing 302 illustrated in FIG. 3 .).

Upon receiving an indication from the virtual marketplace 124 of thepurchasing entity 126 purchasing the item 106, the finance component 212is configured to interface with a financial instruction associated withthe purchasing entity 126 to verify that the purchasing entity 126 hassufficient funds to purchase the item 106 and, if so, contracts with ashipping entity for transporting the item 106 to the purchasing entity126. In some implementations, the logistics component 214 is configuredto select a particular shipping entity and shipping method fortransporting the item 106 to the purchasing entity 126 based on variousconsiderations, such as a price willing to be paid for shipping by thepurchasing entity 126, a shipping speed desired by the purchasing entity126, a cost for the autonomous item generation system 104 to transportthe item 106 to the purchasing entity 126, combinations thereof, and soforth. Thus, through inclusion of the listing component 210, the financecomponent 212, and the logistics component 214, the transaction module112 is configured to automatically handle interactions with the virtualmarketplace 124 that otherwise cannot be performed by conventionalsystems absent human user intervention in facilitating the publicationof the item listing 108 as well as sale activities involved withfacilitating a sale of the subject item 106 for the item listing 108.

The feedback module 114 is configured to receive listing feedback data216 from the virtual marketplace 124, which is representative ofanalytics data provided by the virtual marketplace 124 describing one ormore interactions with the item listing 108. For instance, using theexample item listing 302 of FIG. 3 , the listing feedback data 216 mayspecify different interactions with the item listing 302 such as anumber of page views, or impressions of the item listing 302, a numberof different computing devices that viewed the item listing 302, anumber of favorites of the item listing 302, a number of purchases ofthe subject item of the item listing 302, a number of shares of the itemlisting 302, and so forth. For each of these example interactions, thelisting feedback data 216 may further provide information describing auser profile associated with the interaction, such as a location of theuser during the interaction, a date and time associated with theinteraction, demographic information for the user profile (e.g., age,gender, etc.), historical user behavior data for the user profilerelative to the virtual marketplace 124, combinations thereof, and soforth.

The listing feedback data 216 may provide additional levels of detailregarding interactions with the item listing 108. For instance, thelisting feedback data 216 may specify an amount of time spent viewingdiscrete portions of the item listing 302, such as a duration spentreading the detailed description 310, a number of user reviews displayedin navigating the user reviews portion 316, a purchase of an item listedin the similar items portion 318 instead of the subject item of the itemlisting 302, and so forth. In this manner, the listing feedback data 216is representative of any type and format of data that indicates a mannerin which the item listing 108 was experienced or interacted with byusers of the virtual marketplace 124.

Given the listing feedback data 216, the feedback module 114 isconfigured to generate at least one training dataset 218 for use inrefining the machine learning model 118. To do so, the feedback modulecombines the listing feedback data 216 with the item data 202 in aformat that corresponds to training dataset format used to originallytrain the machine learning model 118 (e.g., the format of the predictedfeedback data included in the training dataset generated by the trainingmodule 116). By virtue of its initial training, the feedback module 114does not need to annotate or otherwise label the training dataset 218(e.g., as quantifying or otherwise classifying the listing feedback data216 as indicating that the item listing 108 is associated with positiveor negative feedback).

Instead, by being trained to identify aspects of information included ininitial training dataset counterparts to the listing feedback data 216represented in the training dataset 218, the machine learning model 118is configured to infer relationships between different aspects of theitem data 202 and the resulting interactions with the item listing 108via the virtual marketplace. To do so, the training module 116 feeds thetraining dataset 218 as an input to the machine learning model 118,which causes the machine learning model 118 to modify one or morecontrol parameters (e.g., internal model node weights) according to aloss function for the model that penalizes negative differences betweenpredicted and observed feedback data. The machine learning model 118with its one or more modified parameters is output by the trainingmodule 116 as the refined machine learning model 220, which iscommunicated to the item generation module 110 for use in place of themachine learning model 118 in subsequently generating item data 202 fora different item 106. As an example of an item 106 and item listing 108subsequently output by the refined machine learning model 220, considerFIG. 4 .

FIG. 4 depicts an example interface 400 of the virtual marketplace 124as displaying an item listing 402, representative of an instance of anitem listing 108 generated from item data 202 output by the refinedmachine learning model 220. Specifically, item listing 402 representsexample changes between item data 202 output by the machine leaningmodel 118 and item data 202 output by the refined machine learning model220. For instance, item listing 402 include an item title 404 for a“Men's Double Pocket Tailored Shirt,” a digital rendering 406 of thesubject item of the item listing 402, a price 408 for the subject item,and a detailed description 410 for the subject item, which each differfrom their counterpart aspects of the item listing 302. Such changes maybe indicative of the machine learning model 118 interpreting the listingfeedback data 216 as indicating certain trends gleaned from interactionswith the virtual marketplace 124, such as that double pocketed men'scollared shirts are currently more popular than collared shirts withoutpockets, that articles of clothing including tags noting that theclothing is “tailored” are associated with positive feedback, that itemlistings featuring multiple digital renderings of the subject item areassociated with increased impression and purchase rates, that therevised layout of item listing 402 is preferred over the layout oflisting 302, and so forth.

To configure the machine learning model 118 to recognize and adapt tosuch changing trends, the training module 116 is configured to identifycontrol parameters in the latent space(s) of the machine learning model118 that correlate with different design aspects (e.g., sleeve length,pocket styles, listing tags, item fabric(s), and so forth. In thismanner, by informing the machine learning model 118 of informationincluded in the listing feedback data 216 via the training datasets 218,the autonomous item generation system 104 is configured to adapt tochanging trends and alter fabrication instructions 120 and item listing108 characteristics for items subsequently generated by the refinedmachine learning model 220 automatically and without relying on guidinguser intervention.

Thus, the autonomous item generation system 104 is configured tocontinuously monitor activities associated with item listings 108published to the virtual marketplace 124 and refine control parametersof the machine learning model 118 used to generate the item listing 108to adapt to inferred trends and behaviors. Because the autonomous itemgeneration system 104 is configured to perform its continuous cycle ofoperations independent of user input and identify trends and behaviorsto consider in refining machine learning model parameters before suchtrends or behaviors can be identified by a user of the autonomous itemgeneration system 104, the autonomous item generation system 104 isconfigured to output a user interface that enables a user to gleaninsight into the system's operations.

FIG. 5 depicts an example interface 500 of the autonomous itemgeneration system 104 configured for output on a display device of thecomputing device implementing the autonomous item generation system 104,such as a display device of computing device 102. The interface 500includes a model selection control 502 and an audience specificationcontrol 504. The audience specification control 504 enables a user ofthe autonomous item generation system 104 to change input parametersconsidered by the model selected via model selection control 502, suchthat the user can observe how the changed input parameters alter aresulting item 106 generated by the machine learning model 118. Forinstance, responsive to receiving selection of one or more models viathe model selection control 502 and a selection of one or more optionsfor defining an audience via the audience specification control 504, theautonomous item generation system updates interface 500 to output itempreview 506, which includes a preview digital rendering 508 of an item106 that would be generated by machine learning model 118 according toinput parameters specified by the selection(s) made with respect tocontrols 502 and 504. Although the illustrated example depicts a previewdigital rendering 508 as being output in the item preview 506 portion ofthe interface 500, the item preview 506 is configured to include adisplay of any information included in item data 202, such as visualrepresentations of the item data 202, textual descriptions of the itemdata 202, and combinations thereof.

In the illustrated example, model selection control 502 includes options510, 512, and 514, where option 510 enables selection of an instance ofmachine learning model 118 trained to generate item data 202 for men'sclothing items, option 512 enables selection of an instance of machinelearning model 118 trained to generate item data 202 for works of art,and option 514 enables a user of the autonomous item generation system104 to upload their own model (e.g., an instance of the machine learningmodel 118 trained to generate item data 202 for an item 106 notcategorized as men's clothing or works of art).

The audience specification control 504 in the illustrated example ofFIG. 5 includes options 516, 518, and 520, where option 516 enablesspecification of a “general public” audience segment (e.g., noconstraints on the audience to be considered by the machine learningmodel 118), control 518 enables designation of custom demographicparameters to be considered by the machine learning model 118 (e.g., aspecified geographic region for an audience of an item listing 108, aspecified audience age and gender combination, a specified time of dayfor publishing the item listing 108, and so forth), and control 520enables designation of a particular individual user to be considered asthe audience for the machine learning model's 118 generation of the itemdata 202.

By interacting with the controls 502 and 504 of interface 500, a user ofthe autonomous item generation system 104 is informed of considerationsmade by the autonomous item generation system 104 in performing itsautomatic operations. For instance, interface 500 indicates to a user ofthe autonomous item generation system 104 that an instance of themachine learning model 118 trained to generate art items, whenconsidering the general public as an audience, will generate an item 106that depicts a waterfront dock scene at sunset with certain natureaspects to achieve a realistic, photo-quality appearance, based oncurrent parameters of the machine learning model 118.

In some implementations, the item preview 506 portion of the interface500 may further include information that describes control parameters ofthe machine learning model 118 selected for the specified audience. Forinstance, the item preview portion 506 may specify that the same machinelearning model 118 configured to generate landscape works of art, whentargeting a Swiss audience, selects control parameters for the machinelearning model 118 that promote inclusion of mountains in the landscapeartwork. Conversely, by interacting with the audience specificationcontrol 504 to change the geographic demographic audience fromSwitzerland to Hawaii, the item preview portion 506 may specify thatcontrol parameters emphasizing inclusion of beaches and oceans in thelandscape artwork are to be utilized. In this manner, the interface 500provides a user of the autonomous item generation system 104 withinsight as to what considerations are made when selecting controlparameters for different machine learning models 118, audienceconsiderations, and combinations thereof.

FIG. 6 depicts an example interface 600 of the autonomous itemgeneration system 104, where the selected option of the audiencespecification control 504 has been altered from option 516 to option518, indicating that specific audience demographic characteristics (notshown) are to be considered instead of the general public considered inthe example interface 500. In the illustrated example, content of theitem preview 506 is altered to indicate how a resulting item 106generated by the same machine learning model 118 would differ based onthe specified audience demographic characteristics. Specifically,interface 600 indicates to the user of the autonomous item generationsystem 104 that the same instance of the machine learning model 118trained to generate art items as selected in FIG. 5 , when consideringthe updated audience demographic characteristics, would instead generatean item 106 that depicts a surreal mountain landscape scene. In thismanner, an interface of the autonomous item generation system 104provides a user with tools to obtain insight regarding the ongoingrevision of a particular machine learning model 118 implemented by theautonomous item generation system 104 in a manner that would not bepossible by inspecting raw input and output data from the machinelearning model 118.

Having considered example details of automatically generating datauseable to fabricate an item 106 and generate a listing for the item tobe published at a virtual marketplace, consider now example proceduresto illustrate aspects of the techniques described herein.

Example Procedures

The following discussion describes techniques that may be implementedutilizing the previously described systems and devices. Aspects of eachof the procedures may be implemented in hardware, firmware, software, ora combination thereof. The procedures are shown as a set of blocks thatspecify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In portions of the following discussion,reference may be made to FIGS. 1-6 .

FIG. 7 depicts a procedure 700 in an example implementation ofautonomous item and item listing generation in accordance with aspectsof the techniques described herein. Notably, each and every operation ofprocedure 700 is performed automatically and independent of user inputor intervention. Using at least one machine learning model, fabricationinstructions for an item and metadata describing the item are generated(block 702). The item generation module 110 of the autonomous itemgeneration system 104, for instance, causes machine learning model 118to generate item data 202, which includes fabrication instructions 120that are useable by the fabrication device 122 to fabricate a tangibleitem 106. In addition to the fabrication instructions, the item data 202includes metadata describing the item 106, such as item description 204,item tags 206, and item pricing data 208.

Fabrication of the item is caused by transmitting the fabricationinstructions to the fabrication device (block 704). The item generationmodule 110, for instance transmits the fabrication instructions 120 tothe fabrication device 122 in a manner that causes the fabricationdevice to fabricate, manufacture, or otherwise output the item 106. Alisting for the item is then created using the metadata describing theitem (block 706). The transaction module 122 of the autonomous itemgeneration system 104 obtains the item data 202 from the item generationmodule 110 and generates item listing 108, such as the example itemlistings depicted in FIGS. 3 and 4 .

The item listing is published to a virtual marketplace and analyticsdata describing one or more interactions with the item listing isobtained (block 708). The transaction module 112, for instance, employslisting component 210 to interface with the virtual marketplace 124 andpublish the item listing 108 in a manner that makes the item listingdiscoverable on the virtual marketplace 124 (e.g., to a browsing user ofthe virtual marketplace 124, to a search engine indexing the virtualmarketplace 124, and so forth). The feedback module 114 of theautonomous item generation system 104 obtains listing feedback data 216,which is representative of information describing one or moreinteractions with the item listing 108 as published to the virtualmarketplace 124. Example interactions include a number of views (e.g., anumber of impressions) of the item listing 108, a number of purchases ofthe item via the item listing 108, a number of favorites of the itemlisting 108, a number of shares of the item listing 108, user reviewssubmitted for the item listing 108, combinations thereof, and so forth.

The listing feedback data 216 may provide additional levels of detailregarding interactions with the item listing 108. For instance, thelisting feedback data 216 may specify an amount of time spent viewingdiscrete portions of the item listing 302, such as a duration spentreading the detailed description 310, a number of user reviews displayedin navigating the user reviews portion 316, a purchase of an item listedin the similar items portion 318 instead of the subject item of the itemlisting 302, and so forth. In this manner, the listing feedback data 216is representative of any type and format of data that indicates a mannerin which the item listing 108 was experienced or interacted with byusers of the virtual marketplace 124.

Training data is then formed based on the analytics data and one or moreparameters of the at least one machine learning model are modified usingthe training data (block 710). The feedback module 114, for instance,combines the listing feedback data together with the item data 202generated by the machine learning model 118 as training dataset 218. Theformat of training dataset 218 output by the feedback module 114 variesaccording to the machine learning model implanted by the item generationmodule 110 and depends on a format of training datasets used tooriginally train the machine learning model 118. The training dataset isthen passed to the training module 116, which provides the trainingdataset 218 as input to the machine learning model 118. Upon input ofthe training dataset 218, the machine learning model 118 is configuredto process the training dataset 218 according to one or more objectivefunctions upon which the machine learning model 118 was initialized,together with one or more loss functions that penalize negativedifferences between predicted and observed feedback data, therebycausing the machine learning model 118 to refine one or more internalparameters via processing of the training dataset 218. The machinelearning model 118 with its one or more modified parameters is thenoutput as refined machine learning model 220.

Using the at least one machine learning model with one or more modifiedparameters, fabrication instructions for an additional item and metadatadescribing the additional item are generated (block 712). The autonomousitem generation system 104, for instance, performs the operations asdescribed above with respect to block 702, using the refined machinelearning model 220 instead of the machine learning model 118. Operationof procedure 700 then optionally returns to block 704, continuing torefine model parameters based on analytic data describing interactionswith item listings 108 generated by the autonomous item generationsystem 104.

FIG. 8 depicts a procedure 800 in an example implementation ofoutputting a user interface for an autonomous item generation system inaccordance with aspects of the techniques described herein. A display ofa user interface for an autonomous item generation system that includescontrols for specifying a machine learning model to be used ingenerating an item and an audience for the machine learning model toconsider in generating the item is output (block 802). The autonomousitem generation system 104, for instance, outputs interface 500 at adisplay of computing device 102. The interface 500 includes modelselection control 502 and audience specification control 504. The modelselection control 502 enables selection of a particular machine learningmodel 118 to be implemented by the autonomous item generation system 104and the audience specification control 504 enables a user to changeinput parameters considered by the model selected via model selectioncontrol 502 and observe how the changed input parameters alter aresulting item 106 generated by the machine learning model 118.

Input is received at the user interface specifying at least one of themachine learning model to be used, or the audience to be considered, ingenerating the item (block 804). A selection of one or more of options510, 512, or 514 offered by the model selection control 502 and/or oneor more options 516, 518, or 520 of the audience specification control504 is received. The user interface is then updated to display a previewof the item as generated by the selected machine learning model for thespecified audience (block 806). For instance, responsive to receivingselection of one or more models via the model selection control 502 anda selection of one or more options for defining an audience via theaudience specification control 504, the autonomous item generationsystem updates interface 500 to output item preview 506, which includesa preview digital rendering 508 of an item 106 that would be generatedby machine learning model 118 according to input parameters specified bythe selection(s) made with respect to controls 502 and 504. In someimplementations, machine learning model 118 control parameters arealternatively or additionally output in the item preview 506 portion ofthe interface 500.

Operation of procedure 800 then optionally returns to block 804,enabling selection of a different combination of the one or more ofoptions 510, 512, or 514 offered by the model selection control 502and/or one or more options 516, 518, or 520 of the audiencespecification control 504. For example, interface 600 depicts an updateto the item preview 506 from that depicted in the illustrated example ofFIG. 5 , responsive to a different option selected from the audiencespecification control 504. In this manner, the user interface output byprocedure 800 enables a user of the autonomous item generation system104 to glean insight into operations of the autonomous item generationsystem 104 that would otherwise be unable to ascertain from inspectionof raw data inputs to, and outputs from, the machine learning model 118.

Having described example procedures in accordance with one or moreimplementations, consider now an example system and device that can beutilized to implement the various techniques described herein.

System and Device

FIG. 9 illustrates an example system generally at 900 that includes anexample computing device 902 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe autonomous item generation system 104. The computing device 902 maybe, for example, a server of a service provider, a device associatedwith a client (e.g., a client device), an on-chip system, and/or anyother suitable computing device or computing system.

The example computing device 902 includes a processing system 904, oneor more computer-readable media 906, and one or more I/O interface 908that are communicatively coupled, one to another. Although not shown,the computing device 902 may further include a system bus or other dataand command transfer system that couples the various components, one toanother. A system bus can include any one or combination of differentbus structures, such as a memory bus or memory controller, a peripheralbus, a universal serial bus, and/or a processor or local bus thatutilizes any of a variety of bus architectures. A variety of otherexamples are also contemplated, such as control and data lines.

The processing system 904 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 904 is illustrated as including hardware elements 910 that may beconfigured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 910 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 906 is illustrated as includingmemory/storage 912. The memory/storage 912 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 912 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 912 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 906 may be configured in a variety of other waysas further described below.

Input/output interface(s) 908 are representative of functionality toallow a user to enter commands and information to the example serviceprovider device 902, and also allow information to be presented to theuser and/or other components or devices using various input/outputdevices. Examples of input devices include a keyboard, a cursor controldevice (e.g., a mouse), a microphone, a scanner, touch functionality(e.g., capacitive or other sensors that are configured to detectphysical touch), a camera (e.g., which may employ visible or non-visiblewavelengths such as infrared frequencies to recognize movement asgestures that do not involve touch), and so forth. Examples of outputdevices include a display device (e.g., a monitor or projector),speakers, a printer, a network card, tactile-response device, and soforth. Thus, the computing device 902 may be configured in a variety ofways as further described below to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the example computing device 902. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information, incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 902, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 910 and computer-readablemedia 906 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 910. The example computing device 902 maybe configured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the example computingdevice 902 as software may be achieved at least partially in hardware,e.g., through use of computer-readable storage media and/or hardwareelements 910 of the processing system 904. The instructions and/orfunctions may be executable/operable by one or more articles ofmanufacture (for example, one or more example computing devices 902and/or processing systems 904) to implement techniques, modules, andexamples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 902 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 914 via a platform 916 as describedbelow.

The cloud 914 includes and/or is representative of a platform 916 forresources 918. The platform 916 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 914. Theresources 918 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe example computing device 902. Resources 918 can also includeservices provided over the Internet and/or through a subscriber network,such as a cellular or Wi-Fi network.

The platform 916 may abstract resources and functions to connect theexample computing device 902 with other computing devices. The platform916 may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources 918that are implemented via the platform 916. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 900. Forexample, the functionality may be implemented in part on the examplecomputing device 902 as well as via the platform 916 that abstracts thefunctionality of the cloud 914.

Conclusion

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. A method for autonomous item generationimplemented by at least one computing device, the method comprising:displaying a user interface that includes a control for specifying amachine learning model to be used in autonomously generating an item anda control for specifying an audience to be considered in generating theitem; receiving input at the user interface that specifies at least oneof the machine learning model to be used or the audience to beconsidered in generating the item; updating the user interface todisplay a preview representation of the item responsive to receiving theinput; receiving additional input that modifies at least one of themachine learning model to be used or the audience to be considered ingenerating the item; and modifying the preview representation of theitem responsive to receiving the additional input.
 2. The method ofclaim 1, wherein the machine learning model comprises a generativeadversarial network trained to generate printing instructions for athree-dimensional object, the item comprises the three-dimensionalobject, and the preview representation of the item comprises a digitalrendering of the three-dimensional object.
 3. The method of claim 1,wherein the machine learning model comprises a generative adversarialnetwork trained to generate fabrication instructions for fabricating anarticle of clothing, the item comprises the article of clothing, and thepreview representation of the item comprises a digital rendering of thearticle of clothing
 4. The method of claim 1, wherein the machinelearning model comprises a generative adversarial network trained togenerate printing instructions for a two-dimensional piece of art, theitem comprises the two-dimensional piece of art, and the previewrepresentation of the item comprises a digital rendering of the piece ofart.
 5. The method of claim 1, wherein modifying the previewrepresentation of the item comprises changing descriptive informationfor the item without changing a visual appearance of the previewrepresentation of the item.
 6. The method of claim 1, wherein modifyingthe preview representation of the item comprises changing a visualappearance of the preview representation of the item without changingdescriptive information for the item.
 7. The method of claim 1, whereinreceiving input at the user interface that specifies the audience to beconsidered in generating the item comprises receiving informationdescribing one or more demographic characteristics of the audience to beconsidered in generating the item.
 8. A system comprising: one or moreprocessors; and a computer-readable storage medium storing instructionsthat are executable by the one or more processors to perform operationscomprising: displaying a user interface that includes a control forspecifying a machine learning model to be used in autonomouslygenerating an item and a control for specifying an audience to beconsidered in generating the item; receiving input at the user interfacethat specifies at least one of the machine learning model to be used orthe audience to be considered in generating the item; updating the userinterface to display a preview representation of the item responsive toreceiving the input; receiving additional input that modifies at leastone of the machine learning model to be used or the audience to beconsidered in generating the item; and modifying the previewrepresentation of the item responsive to receiving the additional input.9. The system of claim 8, wherein the machine learning model comprises agenerative adversarial network trained to generate printing instructionsfor a three-dimensional object, the item comprises the three-dimensionalobject, and the preview representation of the item comprises a digitalrendering of the three-dimensional object.
 10. The system of claim 8,wherein the machine learning model comprises a generative adversarialnetwork trained to generate fabrication instructions for fabricating anarticle of clothing, the item comprises the article of clothing, and thepreview representation of the item comprises a digital rendering of thearticle of clothing
 11. The system of claim 8, wherein the machinelearning model comprises a generative adversarial network trained togenerate printing instructions for a two-dimensional piece of art, theitem comprises the two-dimensional piece of art, and the previewrepresentation of the item comprises a digital rendering of the piece ofart.
 12. The system of claim 8, wherein modifying the previewrepresentation of the item comprises changing descriptive informationfor the item without changing a visual appearance of the previewrepresentation of the item.
 13. The system of claim 8, wherein modifyingthe preview representation of the item comprises changing a visualappearance of the preview representation of the item without changingdescriptive information for the item.
 14. The system of claim 8, whereinreceiving input at the user interface that specifies the audience to beconsidered in generating the item comprises receiving informationdescribing one or more demographic characteristics of the audience to beconsidered in generating the item.
 15. A computer-readable storagemedium storing instructions that are executable by a processing deviceto perform operations comprising: displaying a user interface thatincludes a control for specifying a machine learning model to be used inautonomously generating an item and a control for specifying an audienceto be considered in generating the item; receiving input at the userinterface that specifies at least one of the machine learning model tobe used or the audience to be considered in generating the item;updating the user interface to display a preview representation of theitem responsive to receiving the input; receiving additional input thatmodifies at least one of the machine learning model to be used or theaudience to be considered in generating the item; and modifying thepreview representation of the item responsive to receiving theadditional input.
 16. The computer-readable storage medium of claim 15,wherein the machine learning model comprises a generative adversarialnetwork trained to generate printing instructions for athree-dimensional object, the item comprises the three-dimensionalobject, and the preview representation of the item comprises a digitalrendering of the three-dimensional object.
 17. The computer-readablestorage medium of claim 15, wherein the machine learning model comprisesa generative adversarial network trained to generate fabricationinstructions for fabricating an article of clothing, the item comprisesthe article of clothing, and the preview representation of the itemcomprises a digital rendering of the article of clothing.
 18. Thecomputer-readable storage medium of claim 15, wherein the machinelearning model comprises a generative adversarial network trained togenerate printing instructions for a two-dimensional piece of art, theitem comprises the two-dimensional piece of art, and the previewrepresentation of the item comprises a digital rendering of the piece ofart.
 19. The computer-readable storage medium of claim 15, whereinmodifying the preview representation of the item comprises changingdescriptive information for the item without changing a visualappearance of the preview representation of the item.
 20. Thecomputer-readable storage medium of claim 15, wherein receiving input atthe user interface that specifies the audience to be considered ingenerating the item comprises receiving information describing one ormore demographic characteristics of the audience to be considered ingenerating the item.